Machine learning (ML) algorithms allows computers to define and apply rules which are not described explicitly by the developer.
You’ll find a great deal of articles specialized in machine learning algorithms. Here’s an effort to generate a “helicopter view” description of how these algorithms are applied in different business areas. A list is not the full list of course.
The very first point is the fact that ML algorithms can help people by helping these to find patterns or dependencies, which are not visible with a human.
Numeric forecasting is apparently probably the most well known area here. For a long time computers were actively used for predicting the behaviour of economic markets. Most models were developed prior to the 1980s, when real estate markets got access to sufficient computational power. Later these technologies spread with industries. Since computing power is cheap now, technology-not only by even businesses for all types of forecasting, such as traffic (people, cars, users), sales forecasting and much more.
Anomaly detection algorithms help people scan a lot of data and identify which cases ought to be checked as anomalies. In finance they’re able to identify fraudulent transactions. In infrastructure monitoring they’ve created it easy to identify troubles before they affect business. It is utilized in manufacturing qc.
The main idea here is that you must not describe each kind of anomaly. You provide a large listing of different known cases (a learning set) to the system and system utilize it for anomaly identifying.
Object clustering algorithms allows to group big quantity of data using massive amount meaningful criteria. A male can’t operate efficiently using more than few hundreds of object with a lot of parameters. Machine are capable of doing clustering more effective, by way of example, for clients / leads qualification, product lists segmentation, customer service cases classification etc.
Recommendations / preferences / behavior prediction algorithms provides for us chance to be efficient getting together with customers or users by providing them the key they need, even if they haven’t seriously considered it before. Recommendation systems works really bad in most of services now, however, this sector will probably be improved rapidly soon.
The other point is that machine learning algorithms can replace people. System makes analysis of people’s actions, build rules basing with this information (i.e. learn from people) and apply this rules acting as opposed to people.
First of all this is about various standard decisions making. There are plenty of activities which require for traditional actions in standard situations. People have “standard decisions” and escalate cases that are not standard. There aren’t any reasons, why machines can’t do this: documents processing, phone calls, bookkeeping, first line customer support etc.
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